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題名 應用深度學習預測銀行盈利能力
Application of deep learning methods for predicting bank profitability作者 段晧偉
Duan, Hao-Wei貢獻者 陳心蘋
Chen, Hsin-Ping
段晧偉
Duan, Hao-Wei關鍵詞 深度學習
時間序列
銀行
盈利能力
Deep Learning
Time Series
Banks
Profitability日期 2024 上傳時間 1-Nov-2024 11:35:37 (UTC+8) 摘要 預測銀行盈利能力一直是一個重要議題，本研究旨在通過深度學習技術—長短期記憶模型（LSTM）和傳統時間序列—向量自我迴歸模型（VAR），對台灣五家商業銀行盈利能力進行預測並比較LSTM與VAR模型的預測準確度，其中銀行盈利能力指標為資產報酬率（ROA）和權益報酬率（ROE）。在本研究的樣本中結果顯示LSTM模型捕捉時間序列數據中的非線性關係和長期依賴性方面優於VAR模型。其次，在樣本中引入總體經濟變數後，LSTM模型的預測準確性度會更進一步提升。這些結果強調了深度學習模型在金融預測中的潛力和應用價值，為銀行管理者、投資者和政策制定者提供了有價值的參考依據，有助於更好地管理銀行風險。
Predicting bank profitability has always been a crucial topic. This study aims to predict the profitability of five Taiwanese commercial banks using deep learning technology—Long Short-Term Memory (LSTM) and traditional time series models—Vector Auto Regression (VAR). The indicators of bank profitability are Return on Assets (ROA) and Return on Equity (ROE). Results show that the LSTM model outperforms VAR in capturing nonlinear relationships and long-term dependencies in time-series data. Furthermore, incorporating macroeconomic variables into the sample further improves LSTM’s predictive accuracy, highlighting its potential and value in financial forecasting, offering valuable insights for bank managers, investors, and policymakers to better manage bank risks.參考文獻 Abreu, M., & Mendes, V. (2001). Commercial Bank Interest Margins and Profitability Evidence from Some EU Countries. Pan-European Conference Jointly Organised by the IEFS-UK & University of Macedonia Economic & Social Sciences, 34, 17-20. Agbeja, O., Adelakun, O. J., & Olufemi, F. I. (2015). Capital adequacy ratio and bank profitability in Nigeria: A linear approach. International Journal of Novel Research in Marketing Management and Economics, 2(3), 91-99. Agustí, M., Costa, I. V. Q., & Altmeyer, P. (2023). Deep vector autoregression for macroeconomic data. IFC Bulletins , 59. Albertazzi, U., & Gambacorta, L. (2009). Bank profitability and the business cycle. Journal of Financial Stability, 5(4), 393-409. Aladwan, M. S. (2015). The impact of bank size on profitability: An empirical study on listed Jordanian commercial banks. European Scientific Journal, 11（34）, 2015. Amir, M., Dzulfadeln, A., & Amri, A. (2022). The Effect of Loan to Deposit Ratio on Return On Assets at PT. Bank Mandiri. Jurnal Ilmiah Manajemen, 15(3), 155-164. Athanasoglou, P. P., Brissimis, S. N., & Delis, M. D. (2008). Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of International Financial Markets, Institutions and Money, 18(2), 121-136. Banapon, A.,& Yotenka, R. (2020). Vector autoregressive modelling of profitability Sharia rural bank in Indonesia. The 2nd International Seminar on Science and Technology, 43-52. Berger, A. N., & Bouwman, C. H. (2013). How does capital affect bank performance during financial crises?. Journal of Financial Economics, 109(1), 146-176. Bikker, J. A., & Hu, H. (2002). Cyclical patterns in profits, provisioning and lending of banks and procyclicality of the new Basel capital requirements. PSL Quarterly Review, 55(221). Bourke, P. (1989). Concentration and other determinants of bank profitability in Europe, North America and Australia. Journal of banking & Finance, 13(1), 65-79. Brummelhuis, R., & Luo, Z. (2019). Bank net interest margin forecasting and capital adequacy stress testing by machine learning techniques. SSRN Journal Bzdok, D., Krzywinski, M., & Altman, N. (2018). Machine learning: Supervised methods. Nature Methods, 15(1), 5. Christaria, F., & Kurnia, R. (2016). The impact of financial ratios, operational efficiency and non-performing loan towards commercial bank profitability. Accounting and Finance Review, 1(1). Dietrich, A., & Wanzenried, G. (2011). Determinants of bank profitability before and during the crisis: Evidence from Switzerland. Journal of International Financial Markets, Institutions and Money, 21(3), 307-327. Dou, W. W., Fang, X., Lo, A. W., & Uhlig, H. (2023). Macro-finance models with nonlinear dynamics. Annual Review of Financial Economics, 15(1), 407-432. Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211. Gerlach, S., Peng, W., & Shu, C. (2005). Macroeconomic conditions and banking performance in Hong Kong SAR: A panel data study. Bank for International Settlements, (22). Green, J., & Zhao, W. (2022). Forecasting earnings and returns: A review of recent advancements. The Journal of Finance and Data Science, 8, 120-137. Gunsel, N. (2005). Financial ratios and the probabilistic prediction of bank failure in North Cyprus. Editorial Advisory Board , 18(2), 191-200. Gu, W., Zhong, Y., Li, S., Wei, C., Dong, L., Wang, Z., & Yan, C. (2024). Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis. arXiv.org. https://arxiv.org/abs/2407.16150 Heffernan, S., & Fu, M. (2008). The determinants of bank performance in China. SSRN Journal Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. Horton, R., Searls, P., & Stone, K. (2014). Integrated performance management. Plan. Budget. Forecast. Contacts, 28 Horváth, R., Seidler, J., & Weill, L. (2014). Bank capital and liquidity creation: Granger-causality evidence. Journal of Financial Services Research, 45, 341-361. Jadhav, J. J., Kathale, A., & Rajpurohit, S. (2021). An impact of capital adequacy ratio on the profitability of private sector banks in India–A study. SSRN Journal Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics & Statistics, 52(2). Kanungo, S., & Jain, S. (2023). A Review on Social Media and Deep Neural Networks for Sentiment Analysis in Emergency Management. In 2023 IEEE International Conference on ICT in Business Industry & Government (pp. 1-7). Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841-851. Kosmidou, K. (2008). The determinants of banks' profits in Greece during the period of EU financial integration. Managerial Finance, 34(3), 146-159. Kothandapani, H. P. (2020). Application of machine learning for predicting us bank deposit growth: A univariate and multivariate analysis of temporal dependencies and macroeconomic interrelationships. Journal of Empirical Social Science Studies, 4(1), 1-20. Limajatini, L., Murwaningsari, E., & Sellawati, S. (2019). Analysis of the effect of loan to deposit ratio, non-performing loan & capital adequacy ratio in profitability: Empirical study of conventional banking companies listed in IDX period 2014–2017. eCo-Fin, 1(2), 55-62. Liu, Y., Dong, S., Lu, M., & Wang, J. (2018). LSTM based reserve prediction for bank outlets. Tsinghua Science and Technology, 24(1), 77-85. Makri, V., Tsagkanos, A., & Bellas, A. (2014). Determinants of non-performing loans: The case of Eurozone. Panoeconomicus, 61(2), 193-206. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115-133. Men, L., Ilk, N., Tang, X., & Liu, Y. (2021). Multi-disease prediction using LSTM recurrent neural networks. Expert Systems with Applications, 177. Molyneux, P., & Thornton, J. (1992). Determinants of European bank profitability: A note. Journal of banking & Finance, 16(6), 1173-1178. Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE International Conference on Machine Learning and Applications, 1394-1401. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48. Smith, R., Staikouras, C., & Wood, G. (2003). Non-interest income and total income stability. SSRN Journal. Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. Journal of Economic perspectives, 15(4), 101-115. Tan, Y., & Floros, C. (2012). Bank profitability and GDP growth in China: A note. Journal of Chinese Economic and Business Studies, 10(3), 267-273. Pasiouras, F., & Kosmidou, K. (2007). Factors influencing the profitability of domestic and foreign commercial banks in the European Union. Research in International Business and Finance, 21(2), 222-237. Pennacchi, George G., and João AC Santos. "Why do banks target ROE?." Journal of Financial Stability, 54, 100856. Pirani, M., Thakkar, P., Jivrani, P., Bohara, M. H., & Garg, D. (2022). A comparative analysis of ARIMA, GRU, LSTM and BiLSTM on financial time series forecasting.In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics ,1-6. Roman, A., & Tomuleasa, I. (2013). Analysis of profitability determinants: Empirical evidence of commercial banks in the new EU member states. University of Iasi, Romania. Runkle, D. E. (1987). Vector autoregressions and reality. Journal of Business & Economic Statistics, 5(4), 437-442. Zahariev, A., Angelov, P., & Zarkova, S. (2022). Estimation of bank profitability using vector error correction model and support vector regression. Economic Alternatives, 2, 157-170. Zhao, Z., Rao, R., Tu, S., & Shi, J. (2017). Time-weighted LSTM model with redefined labeling for stock trend prediction. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence ,1210-1217. 黃台心、江典霖 （2014） 我國銀行業市場競爭度與金融創新之連結。中央銀行季刊, 36（2）, 15-52. 描述 碩士
國立政治大學
經濟學系
110258043資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110258043 資料類型 thesis dc.contributor.advisor 陳心蘋 zh_TW dc.contributor.advisor Chen, Hsin-Ping en_US dc.contributor.author (Authors) 段晧偉 zh_TW dc.contributor.author (Authors) Duan, Hao-Wei en_US dc.creator (作者) 段晧偉 zh_TW dc.creator (作者) Duan, Hao-Wei en_US dc.date (日期) 2024 en_US dc.date.accessioned 1-Nov-2024 11:35:37 (UTC+8) - dc.date.available 1-Nov-2024 11:35:37 (UTC+8) - dc.date.issued (上傳時間) 1-Nov-2024 11:35:37 (UTC+8) - dc.identifier (Other Identifiers) G0110258043 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/154229 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 110258043 zh_TW dc.description.abstract (摘要) 預測銀行盈利能力一直是一個重要議題，本研究旨在通過深度學習技術—長短期記憶模型（LSTM）和傳統時間序列—向量自我迴歸模型（VAR），對台灣五家商業銀行盈利能力進行預測並比較LSTM與VAR模型的預測準確度，其中銀行盈利能力指標為資產報酬率（ROA）和權益報酬率（ROE）。在本研究的樣本中結果顯示LSTM模型捕捉時間序列數據中的非線性關係和長期依賴性方面優於VAR模型。其次，在樣本中引入總體經濟變數後，LSTM模型的預測準確性度會更進一步提升。這些結果強調了深度學習模型在金融預測中的潛力和應用價值，為銀行管理者、投資者和政策制定者提供了有價值的參考依據，有助於更好地管理銀行風險。 zh_TW dc.description.abstract (摘要) Predicting bank profitability has always been a crucial topic. This study aims to predict the profitability of five Taiwanese commercial banks using deep learning technology—Long Short-Term Memory (LSTM) and traditional time series models—Vector Auto Regression (VAR). The indicators of bank profitability are Return on Assets (ROA) and Return on Equity (ROE). Results show that the LSTM model outperforms VAR in capturing nonlinear relationships and long-term dependencies in time-series data. Furthermore, incorporating macroeconomic variables into the sample further improves LSTM’s predictive accuracy, highlighting its potential and value in financial forecasting, offering valuable insights for bank managers, investors, and policymakers to better manage bank risks. en_US dc.description.tableofcontents 第一章 緒論 1 第二章 文獻回顧 3 第三章 研究方法 7 第一節 神經網路與長短期記憶模型 7 第二節 向量自我迴歸模型 13 第四章 建構銀行盈利能力之預測模型 16 第一節 資料結構與來源 16 第二節 建構預測銀行盈利能力之模型 21 第五章 實證結果分析 26 第一節 LSTM模型與VAR模型預測結果與比較 26 第二節 總體經濟變數對銀行盈利能力的影響 40 第六章 結論與建議 44 參考文獻 46 附錄 51 zh_TW dc.format.extent 1675459 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110258043 en_US dc.subject (關鍵詞) 深度學習 zh_TW dc.subject (關鍵詞) 時間序列 zh_TW dc.subject (關鍵詞) 銀行 zh_TW dc.subject (關鍵詞) 盈利能力 zh_TW dc.subject (關鍵詞) Deep Learning en_US dc.subject (關鍵詞) Time Series en_US dc.subject (關鍵詞) Banks en_US dc.subject (關鍵詞) Profitability en_US dc.title (題名) 應用深度學習預測銀行盈利能力 zh_TW dc.title (題名) Application of deep learning methods for predicting bank profitability en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Abreu, M., & Mendes, V. (2001). Commercial Bank Interest Margins and Profitability Evidence from Some EU Countries. Pan-European Conference Jointly Organised by the IEFS-UK & University of Macedonia Economic & Social Sciences, 34, 17-20. Agbeja, O., Adelakun, O. J., & Olufemi, F. I. (2015). Capital adequacy ratio and bank profitability in Nigeria: A linear approach. International Journal of Novel Research in Marketing Management and Economics, 2(3), 91-99. Agustí, M., Costa, I. V. Q., & Altmeyer, P. (2023). Deep vector autoregression for macroeconomic data. IFC Bulletins , 59. Albertazzi, U., & Gambacorta, L. (2009). Bank profitability and the business cycle. Journal of Financial Stability, 5(4), 393-409. Aladwan, M. S. (2015). The impact of bank size on profitability: An empirical study on listed Jordanian commercial banks. European Scientific Journal, 11（34）, 2015. Amir, M., Dzulfadeln, A., & Amri, A. (2022). The Effect of Loan to Deposit Ratio on Return On Assets at PT. Bank Mandiri. Jurnal Ilmiah Manajemen, 15(3), 155-164. Athanasoglou, P. P., Brissimis, S. N., & Delis, M. D. (2008). Bank-specific, industry-specific and macroeconomic determinants of bank profitability. Journal of International Financial Markets, Institutions and Money, 18(2), 121-136. Banapon, A.,& Yotenka, R. (2020). Vector autoregressive modelling of profitability Sharia rural bank in Indonesia. The 2nd International Seminar on Science and Technology, 43-52. Berger, A. N., & Bouwman, C. H. (2013). How does capital affect bank performance during financial crises?. Journal of Financial Economics, 109(1), 146-176. Bikker, J. A., & Hu, H. (2002). Cyclical patterns in profits, provisioning and lending of banks and procyclicality of the new Basel capital requirements. PSL Quarterly Review, 55(221). Bourke, P. (1989). Concentration and other determinants of bank profitability in Europe, North America and Australia. Journal of banking & Finance, 13(1), 65-79. Brummelhuis, R., & Luo, Z. (2019). Bank net interest margin forecasting and capital adequacy stress testing by machine learning techniques. SSRN Journal Bzdok, D., Krzywinski, M., & Altman, N. (2018). Machine learning: Supervised methods. Nature Methods, 15(1), 5. Christaria, F., & Kurnia, R. (2016). The impact of financial ratios, operational efficiency and non-performing loan towards commercial bank profitability. Accounting and Finance Review, 1(1). Dietrich, A., & Wanzenried, G. (2011). Determinants of bank profitability before and during the crisis: Evidence from Switzerland. Journal of International Financial Markets, Institutions and Money, 21(3), 307-327. Dou, W. W., Fang, X., Lo, A. W., & Uhlig, H. (2023). Macro-finance models with nonlinear dynamics. Annual Review of Financial Economics, 15(1), 407-432. Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211. Gerlach, S., Peng, W., & Shu, C. (2005). Macroeconomic conditions and banking performance in Hong Kong SAR: A panel data study. Bank for International Settlements, (22). Green, J., & Zhao, W. (2022). Forecasting earnings and returns: A review of recent advancements. The Journal of Finance and Data Science, 8, 120-137. Gunsel, N. (2005). Financial ratios and the probabilistic prediction of bank failure in North Cyprus. Editorial Advisory Board , 18(2), 191-200. Gu, W., Zhong, Y., Li, S., Wei, C., Dong, L., Wang, Z., & Yan, C. (2024). Predicting Stock Prices with FinBERT-LSTM: Integrating News Sentiment Analysis. arXiv.org. https://arxiv.org/abs/2407.16150 Heffernan, S., & Fu, M. (2008). The determinants of bank performance in China. SSRN Journal Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. Horton, R., Searls, P., & Stone, K. (2014). Integrated performance management. Plan. Budget. Forecast. Contacts, 28 Horváth, R., Seidler, J., & Weill, L. (2014). Bank capital and liquidity creation: Granger-causality evidence. Journal of Financial Services Research, 45, 341-361. Jadhav, J. J., Kathale, A., & Rajpurohit, S. (2021). An impact of capital adequacy ratio on the profitability of private sector banks in India–A study. SSRN Journal Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics & Statistics, 52(2). Kanungo, S., & Jain, S. (2023). A Review on Social Media and Deep Neural Networks for Sentiment Analysis in Emergency Management. In 2023 IEEE International Conference on ICT in Business Industry & Government (pp. 1-7). Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid, 10(1), 841-851. Kosmidou, K. (2008). The determinants of banks' profits in Greece during the period of EU financial integration. Managerial Finance, 34(3), 146-159. Kothandapani, H. P. (2020). Application of machine learning for predicting us bank deposit growth: A univariate and multivariate analysis of temporal dependencies and macroeconomic interrelationships. Journal of Empirical Social Science Studies, 4(1), 1-20. Limajatini, L., Murwaningsari, E., & Sellawati, S. (2019). Analysis of the effect of loan to deposit ratio, non-performing loan & capital adequacy ratio in profitability: Empirical study of conventional banking companies listed in IDX period 2014–2017. eCo-Fin, 1(2), 55-62. Liu, Y., Dong, S., Lu, M., & Wang, J. (2018). LSTM based reserve prediction for bank outlets. Tsinghua Science and Technology, 24(1), 77-85. Makri, V., Tsagkanos, A., & Bellas, A. (2014). Determinants of non-performing loans: The case of Eurozone. Panoeconomicus, 61(2), 193-206. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5, 115-133. Men, L., Ilk, N., Tang, X., & Liu, Y. (2021). Multi-disease prediction using LSTM recurrent neural networks. Expert Systems with Applications, 177. Molyneux, P., & Thornton, J. (1992). Determinants of European bank profitability: A note. Journal of banking & Finance, 16(6), 1173-1178. Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE International Conference on Machine Learning and Applications, 1394-1401. Sims, C. A. (1980). Macroeconomics and reality. Econometrica, 48(1), 1-48. Smith, R., Staikouras, C., & Wood, G. (2003). Non-interest income and total income stability. SSRN Journal. Stock, J. H., & Watson, M. W. (2001). Vector autoregressions. Journal of Economic perspectives, 15(4), 101-115. Tan, Y., & Floros, C. (2012). Bank profitability and GDP growth in China: A note. Journal of Chinese Economic and Business Studies, 10(3), 267-273. Pasiouras, F., & Kosmidou, K. (2007). Factors influencing the profitability of domestic and foreign commercial banks in the European Union. Research in International Business and Finance, 21(2), 222-237. Pennacchi, George G., and João AC Santos. "Why do banks target ROE?." Journal of Financial Stability, 54, 100856. Pirani, M., Thakkar, P., Jivrani, P., Bohara, M. H., & Garg, D. (2022). A comparative analysis of ARIMA, GRU, LSTM and BiLSTM on financial time series forecasting.In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics ,1-6. Roman, A., & Tomuleasa, I. (2013). Analysis of profitability determinants: Empirical evidence of commercial banks in the new EU member states. University of Iasi, Romania. Runkle, D. E. (1987). Vector autoregressions and reality. Journal of Business & Economic Statistics, 5(4), 437-442. Zahariev, A., Angelov, P., & Zarkova, S. (2022). Estimation of bank profitability using vector error correction model and support vector regression. Economic Alternatives, 2, 157-170. Zhao, Z., Rao, R., Tu, S., & Shi, J. (2017). Time-weighted LSTM model with redefined labeling for stock trend prediction. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence ,1210-1217. 黃台心、江典霖 （2014） 我國銀行業市場競爭度與金融創新之連結。中央銀行季刊, 36（2）, 15-52. zh_TW
